Predicting pregnancy status from mid-infrared spectroscopy in dairy cow milk using deep learning.

Accurately identifying pregnancy status is imperative for a profitable dairy enterprise. Mid-infrared (MIR) spectroscopy is routinely used to determine fat and protein concentrations in milk samples. Mid-infrared spectra have successfully been used to predict other economically important traits, including fatty acid content, mineral content, body energy status, lactoferrin, feed intake, and methane emissions. Machine learning has been used in a variety of fields to find patterns in vast quantities of data. This study aims to use deep learning, a sub-branch of machine learning, to establish pregnancy status from routinely collected milk MIR spectral data. Milk spectral data were obtained from National Milk Records (Chippenham, UK), who collect large volumes of data continuously on a monthly basis. Two approaches were followed: using genetic algorithms for feature selection and network design (model 1), and transfer learning with a pretrained DenseNet model (model 2). Feature selection in model 1 showed that the number of wave points in MIR data could be reduced from 1,060 to 196 wave points. The trained model converged after 162 epochs with validation accuracy and loss of 0.89 and 0.18, respectively. Although the accuracy was sufficiently high, the loss (in terms of predicting only 2 labels) was considered too high and suggested that the model would not be robust enough to apply to industry. Model 2 was trained in 2 stages of 100 epochs each with spectral data converted to gray-scale images and resulted in accuracy and loss of 0.97 and 0.08, respectively. Inspection on inference data showed prediction sensitivity of 0.89, specificity of 0.86, and prediction accuracy of 0.88. Results indicate that milk MIR data contains features relating to pregnancy status and the underlying metabolic changes in dairy cows, and such features can be identified by means of deep learning. Prediction equations from trained models can be used to alert farmers of nonviable pregnancies as well as to verify conception dates.

Predicting bovine tuberculosis status of dairy cows from mid-infrared spectral data of milk using deep learning.

Bovine tuberculosis (bTB) is a zoonotic disease in cattle that is transmissible to humans, distributed worldwide, and considered endemic throughout much of England and Wales. Mid-infrared (MIR) analysis of milk is used routinely to predict fat and protein concentration, and is also a robust predictor of several other economically important traits including individual fatty acids and body energy. This study predicted bTB status of UK dairy cows using their MIR spectral profiles collected as part of routine milk recording. Bovine tuberculosis data were collected as part of the national bTB testing program for Scotland, England, and Wales; these data provided information from over 40,500 bTB herd breakdowns. Corresponding individual cow life-history data were also available and provided information on births, movements, and deaths of all cows in the study. Data relating to single intradermal comparative cervical tuberculin (SICCT) skin-test results, culture, slaughter status, and presence of lesions were combined to create a binary bTB phenotype labeled 0 to represent nonresponders (i.e., healthy cows) and 1 to represent responders (i.e., bTB-affected cows). Contemporaneous individual milk MIR spectral data were collected as part of monthly routine milk recording and matched to bTB status of individual animals on the single intradermal comparative cervical tuberculin test date (±15 d). Deep learning, a sub-branch of machine learning, was used to train artificial neural networks and develop a prediction pipeline for subsequent use in national herds as part of routine milk recording. Spectra were first converted to 53 × 20-pixel PNG images, then used to train a deep convolutional neural network. Deep convolutional neural networks resulted in a bTB prediction accuracy (i.e., the number of correct predictions divided by the total number of predictions) of 71% after training for 278 epochs. This was accompanied by both a low validation loss (0.71) and moderate sensitivity and specificity (0.79 and 0.65, respectively). To balance data in each class, additional training data were synthesized using the synthetic minority over sampling technique. Accuracy was further increased to 95% (after 295 epochs), with corresponding validation loss minimized (0.26), when synthesized data were included during training of the network. Sensitivity and specificity also saw a 1.22- and 1.45-fold increase to 0.96 and 0.94, respectively, when synthesized data were included during training. We believe this study to be the first of its kind to predict bTB status from milk MIR spectral data. We also believe it to be the first study to use milk MIR spectral data to predict a disease phenotype, and posit that the automated prediction of bTB status at routine milk recording could provide farmers with a robust tool that enables them to make early management decisions on potential reactor cows, and thus help slow the spread of bTB.

Energy profiling of dairy cows from routine milk mid-infrared analysis.

The balance of body energy within and across lactations can have health and fertility consequences for the dairy cow. This study aimed to create a large calibration data set of dairy cow body energy traits across the cow's productive life, with concurrent milk mid-infrared (MIR) spectral data, to generate a prediction tool for use in commercial dairy herds. Detailed phenotypic data from 1,101 Holstein Friesian cows from the Langhill research herd (SRUC, Scotland) were used to generate energy balance (EB) and effective energy intake (EI), both in megajoules per day. Pretreatment of spectral data involved standardization to account for drift over time and machine. Body energy estimates were aligned with their spectral data to generate a prediction of these traits based on milk MIR spectroscopy. After data edits, partial least squares analysis generated prediction equations with a coefficient of determination from split sample 10-fold cross validation of 0.77 and 0.75 for EB and EI, respectively. These prediction equations were applied to national milk MIR spectra on over 11 million animal test dates (January 2013 to December 2016) from 4,453 farms. The predictions generated from these were subject to phenotypic analyses with a fixed regression model highlighting differences between the main dairy breeds in terms of energy traits. Genetic analyses generated heritability estimates for EB and EI ranging from 0.12 to 0.17 and 0.13 to 0.15, respectively. This study shows that MIR-based predictions from routinely collected national data can be used to generate predictions of dairy cow energy turnover profiles for both animal management and genetic improvement of such difficult and expensive-to-record traits.

Genetic evaluation for bovine tuberculosis resistance in dairy cattle.

Genetic evaluations for resistance to bovine tuberculosis (bTB) were calculated based on British national data including individual animal tuberculin skin test results, postmortem examination (presence of bTB lesions and bacteriological culture for Mycobacterium bovis), animal movement and location information, production history, and pedigree records. Holstein cows with identified sires in herds with bTB breakdowns (new herd incidents) occurring between the years 2000 and 2014 were considered. In the first instance, cows with a positive reaction to the skin test and a positive postmortem examination were defined as infected. Values of 0 and 1 were assigned to healthy and infected animal records, respectively. Data were analyzed with mixed models. Linear and logit function heritability estimates were 0.092 and 0.172, respectively. In subsequent analyses, breakdowns were split into 2-mo intervals to better model time of exposure and infection in the contemporary group. Intervals with at least one infected individual were retained and multiple intervals within the same breakdown were included. Healthy animal records were assigned values of 0, and infected records a value of 1 in the interval of infection and values reflecting a diminishing probability of infection in the preceding intervals. Heritability and repeatability estimates were 0.115 and 0.699, respectively. Reliabilities and across time stability of the genetic evaluation were improved with the interval model. Subsequently, 2 more definitions of "infected" were analyzed with the interval model: (1) all positive skin test reactors regardless of postmortem examination, and (2) all positive skin test reactors plus nonreactors with positive postmortem examination. Estimated heritability was 0.085 and 0.089, respectively; corresponding repeatability estimates were 0.701 and 0.697. Genetic evaluation reliabilities and across time stability did not change. Correlations of genetic evaluations for bTB with other traits in the current breeding goal were mostly not different from zero. Correlation with the UK Profitable Lifetime Index was moderate, significant, and favorable. Results demonstrated the feasibility of a national genetic evaluation for bTB resistance. Selection for enhanced resistance will have a positive effect on profitability and no antagonistic effects on current breeding goal traits. Official genetic evaluations are now based on the interval model and the last bTB trait definition.

The genetic and biological basis of feed efficiency in mid-lactation Holstein dairy cows.

The objective of this study was to identify genomic regions and candidate genes associated with feed efficiency in lactating Holstein cows. In total, 4,916 cows with actual or imputed genotypes for 60,671 single nucleotide polymorphisms having individual feed intake, milk yield, milk composition, and body weight records were used in this study. Cows were from research herds located in the United States, Canada, the Netherlands, and the United Kingdom. Feed efficiency, defined as residual feed intake (RFI), was calculated within location as the residual of the regression of dry matter intake (DMI) on milk energy (MilkE), metabolic body weight (MBW), change in body weight, and systematic effects. For RFI, DMI, MilkE, and MBW, bivariate analyses were performed considering each trait as a separate trait within parity group to estimate variance components and genetic correlations between them. Animal relationships were established using a genomic relationship matrix. Genome-wide association studies were performed separately by parity group for RFI, DMI, MilkE, and MBW using the Bayes B method with a prior assumption that 1% of single nucleotide polymorphisms have a nonzero effect. One-megabase windows with greatest percentage of the total genetic variation explained by the markers (TGVM) were identified, and adjacent windows with large proportion of the TGVM were combined and reanalyzed. Heritability estimates for RFI were 0.14 (±0.03; ±SE) in primiparous cows and 0.13 (±0.03) in multiparous cows. Genetic correlations between primiparous and multiparous cows were 0.76 for RFI, 0.78 for DMI, 0.92 for MBW, and 0.61 for MilkE. No single 1-Mb window explained a significant proportion of the TGVM for RFI; however, after combining windows, significance was met on Bos taurus autosome 27 in primiparous cows, and nearly reached on Bos taurus autosome 4 in multiparous cows. Among other genes, these regions contain ß-3 adrenergic receptor and the physiological candidate gene, leptin, respectively. Between the 2 parity groups, 3 of the 10 windows with the largest effects on DMI neighbored windows affecting RFI, but were not in the top 10 regions for MilkE or MBW. This result suggests a genetic basis for feed intake that is unrelated to energy consumption required for milk production or expected maintenance as determined by MBW. In conclusion, feed efficiency measured as RFI is a polygenic trait exhibiting a dynamic genetic basis and genetic variation distinct from that underlying expected maintenance requirements and milk energy output.

Hot topic: Definition and implementation of a breeding value for feed efficiency in dairy cows.

A new breeding value that combines the amount of feed saved through improved metabolic efficiency with predicted maintenance requirements is described. The breeding value includes a genomic component for residual feed intake (RFI) combined with maintenance requirements calculated from either a genomic or pedigree estimated breeding value (EBV) for body weight (BW) predicted using conformation traits. Residual feed intake is only available for genotyped Holsteins; however, BW is available for all breeds. The RFI component of the "feed saved" EBV has 2 parts: Australian calf RFI and Australian lactating cow RFI. Genomic breeding values for RFI were estimated from a reference population of 2,036 individuals in a multi-trait analysis including Australian calf RFI (n=843), Australian lactating cow RFI (n=234), and UK and Dutch lactating cow RFI (n=958). In all cases, the RFI phenotypes were deviations from a mean of 0, calculated by correcting dry matter intake for BW, growth, and milk yield (in the case of lactating cows). Single nucleotide polymorphism effects were calculated from the output of genomic BLUP and used to predict breeding values of 4,106 Holstein sires that were genotyped but did not have RFI phenotypes themselves. These bulls already had BW breeding values calculated from type traits, from which maintenance requirements in kilograms of feed per year were inferred. Finally, RFI and the feed required for maintenance (through BW) were used to calculate a feed saved breeding value and expressed as the predicted amount of feed saved per year. Animals that were 1 standard deviation above the mean were predicted to eat 66 kg dry matter less per year at the same level of milk production. In a data set of genotyped Holstein sires, the mean reliability of the feed saved breeding value was 0.37. For Holsteins that are not genotyped and for breeds other than Holsteins, feed saved is calculated using BW only. From April 2015, feed saved has been included as part of the Australian national selection index, the Balanced Performance Index (BPI). Selection on the BPI is expected to lead to modest gains in feed efficiency.

On the value of the phenotypes in the genomic era.

Genetic improvement programs around the world rely on the collection of accurate phenotypic data. These phenotypes have an inherent value that can be estimated as the contribution of an additional record to genetic gain. Here, the contribution of phenotypes to genetic gain was calculated using traditional progeny testing (PT) and 2 genomic selection (GS) strategies that, for simplicity, included either males or females in the reference population. A procedure to estimate the theoretical economic contribution of a phenotype to a breeding program is described for both GS and PT breeding programs through the increment in genetic gain per unit of increase in estimated breeding value reliability obtained when an additional phenotypic record is added. The main factors affecting the value of a phenotype were the economic value of the trait, the number of phenotypic records already available for the trait, and its heritability. Furthermore, the value of a phenotype was affected by several other factors, including the cost of establishing the breeding program and the cost of phenotyping and genotyping. The cost of achieving a reliability of 0.60 was assessed for different reference populations for GS. Genomic reference populations of more sires with small progeny group sizes (e.g., 20 equivalent daughters) had a lower cost than those reference populations with either large progeny group sizes for fewer genotyped sires, or female reference populations, unless the heritability was large and the cost of phenotyping exceeded a few hundred dollars; then, female reference populations were preferable from an economic perspective.

Short communication: Validation of genomic breeding value predictions for feed intake and feed efficiency traits.

Validating genomic prediction equations in independent populations is an important part of evaluating genomic selection. Published genomic predictions from 2 studies on (1) residual feed intake and (2) dry matter intake (DMI) were validated in a cohort of 78 multiparous Holsteins from Australia. The mean realized accuracy of genomic prediction for residual feed intake was 0.27 when the reference population included phenotypes from 939 New Zealand and 843 Australian growing heifers (aged 5-8 mo) genotyped on high density (770k) single nucleotide polymorphism chips. The 90% bootstrapped confidence interval of this estimate was between 0.16 and 0.36. The mean realized accuracy was slightly lower (0.25) when the reference population comprised only Australian growing heifers. Higher realized accuracies were achieved for DMI in the same validation population and using a multicountry model that included 958 lactating cows from the Netherlands and United Kingdom in addition to 843 growing heifers from Australia. The multicountry analysis for DMI generated 3 sets of genomic predictions for validation animals, one on each country scale. The highest mean accuracy (0.72) was obtained when the genomic breeding values were expressed on the Dutch scale. Although the validation population used in this study was small (n=78), the results illustrate that genomic selection for DMI and residual feed intake is feasible. Multicountry collaboration in the area of dairy cow feed efficiency is the evident pathway to achieving reasonable genomic prediction accuracies for these valuable traits.

International genetic evaluations for feed intake in dairy cattle through the collation of data from multiple sources.

Feed represents a large proportion of the variable costs in dairy production systems. The omission of feed intake measures explicitly from national dairy cow breeding objectives is predominantly due to a lack of information from which to make selection decisions. However, individual cow feed intake data are available in different countries, mostly from research or nucleus herds. None of these data sets are sufficiently large enough on their own to generate accurate genetic evaluations. In the current study, we collate data from 10 populations in 9 countries and estimate genetic parameters for dry matter intake (DMI). A total of 224,174 test-day records from 10,068 parity 1 to 5 records of 6,957 cows were available, as well as records from 1,784 growing heifers. Random regression models were fit to the lactating cow test-day records and predicted feed intake at 70 d postcalving was extracted from these fitted profiles. The random regression model included a fixed polynomial regression for each lactation separately, as well as herd-year-season of calving and experimental treatment as fixed effects; random effects fit in the model included individual animal deviation from the fixed regression for each parity as well as mean herd-specific deviations from the fixed regression. Predicted DMI at 70 d postcalving was used as the phenotype for the subsequent genetic analyses undertaken using an animal repeatability model. Heritability estimates of predicted cow feed intake 70 d postcalving was 0.34 across the entire data set and varied, within population, from 0.08 to 0.52. Repeatability of feed intake across lactations was 0.66. Heritability of feed intake in the growing heifers was 0.20 to 0.34 in the 2 populations with heifer data. The genetic correlation between feed intake in lactating cows and growing heifers was 0.67. A combined pedigree and genomic relationship matrix was used to improve linkages between populations for the estimation of genetic correlations of DMI in lactating cows; genotype information was available on 5,429 of the animals. Populations were categorized as North America, grazing, other low input, and high input European Union. Albeit associated with large standard errors, genetic correlation estimates for DMI between populations varied from 0.14 to 0.84 but were stronger (0.76 to 0.84) between the populations representative of high-input production systems. Genetic correlations with the grazing populations were weak to moderate, varying from 0.14 to 0.57. Genetic evaluations for DMI can be undertaken using data collated from international populations; however, genotype-by-environment interactions with grazing production systems need to be considered.

Direct and maternal genetic relationships between calving ease, gestation length, milk production, fertility, type, and lifespan of Holstein-Friesian primiparous cows.

As the emphasis in cattle breeding is shifting from traits that increase income toward traits that reduce costs, national breeding indices are expanding to include functional traits such as calving ease (CE). However, one issue is the lack of knowledge of genetic relationships between CE and other dairy traits. The same can be said about gestation length (GL), a potential novel selection trait with considerable heritabilities and possible genetic relationships with the calving process. This study aimed to estimate the genetic relationships between CE, GL, and other dairy traits of interest using a national data set of 31,053 primiparous cow performance records, as well as to separate direct and maternal genetic effects. Chosen dairy traits included fertility (calving interval, days to first service, nonreturn rate after 56 d, number of inseminations per conception), milk production (milk yield at d 110 in milk, accumulated 305-d milk yield, accumulated 305-d fat yield, accumulated 305-d protein yield), type (udder depth, chest width, rump width, rump angle, mammary composition, stature, body depth), and lifespan traits (functional days of productive life). To allow the separation of direct and maternal genetic effects, a random sire of the calf effect was included in the multi-trait linear trivariate sire models fitted using ASReml. Significant results showed that easily born individuals were genetically prone to high milk yield and reduced fertility in first lactation. Difficult calving primiparous cows were likely associated with being high-producing, wide and deep animals, with a reduced ability to subsequently conceive. Individuals that were born relatively early were associated with good genetic merit for milk production. Finally, individuals carrying their offspring longer were genetically associated with being wide and large animals that were themselves born relatively early. The study shows that it is feasible and valuable to separate direct and maternal effects when estimating genetic correlations between calving and other dairy traits. Furthermore, gestation length is best used as an indicator trait for lowly heritable calving traits, rather than as a novel selection trait. As estimated direct and maternal genetic correlations differ, we can conclude that genetic relationships between CE, GL, and traits of interest are present, but caution is required if these traits are implemented in national breeding indices.

The sensitivity of predicted financial and genetic gains in Holsteins to changes in the economic value of traits.

The objective of this study was to assess the impact of using different relative economic values (REVs) in selection indices on predicted financial and trait gains from selection of sires of cows and on the choice of leading Holstein bulls available in the UK dairy industry. Breeding objective traits were milk yield, fat yield, protein yield, lifespan, mastitis, non-return rate, calving interval and lameness. Relative importance of a trait, as estimated by a.h(2), was only moderately related to the rate of financial loss or total economic merit (ΔTEM) per percentage under- or overestimation of REV (r = 0.38 and 0.29, respectively) as a result of the variance-covariance structure of traits. The effects on TEM of under- or overestimating trait REVs were non-symmetrical. TEM was most sensitive to incorrect REVs for protein, fat, milk and lifespan and least sensitive to incorrect calving interval, lameness, non-return and mastitis REVs. A guide to deciding which dairy traits require the most rigorous analysis in the calculation of their REVs is given. Varying the REVs within a fairly wide range resulted in different bulls being selected by index and their differing predicted transmitting abilities would result in the herds moving in different directions in the long term (20 years). It is suggested that customized indices, where the farmer creates rankings of bulls tailored to their specific farm circumstances, can be worthwhile.

Technical note: Prediction of liveweight from linear conformation traits in dairy cattle.

The objectives of this study were to derive phenotypic and genetic prediction equations of liveweight from linear conformation traits, and estimate genetic and phenotypic parameters for these traits. Data pertained to 2,728 conformation and liveweight records of 613 cows in 1,529 lactations. Cows were raised at the Scottish Agricultural College research station and had calved between 2002 and 2010. Fifteen linear conformation traits were considered as predictors. To validate phenotypic predictions, the data set was randomly split into independent reference and validation subsets. Reference subsets were used to derive prediction equations with the use of a mixed model. Comparisons between predicted and actual liveweight in the validation subsets indicated that stature, chest width, body depth, and angularity could be used to derive phenotypic predictions of liveweight. Accuracy of these predictions was better for first-lactation than for all-lactation liveweight data. Significant genetic correlations between liveweight and the 4 predictor traits ranged from 0.49 to 0.76, and phenotypic correlations were 0.33 to 0.56. Estimated genetic (co)variances were used to develop prediction equations of animal genetic merit for liveweight from routinely calculated genetic evaluations for conformation traits.

Validation of mid-infrared spectrometry in milk for predicting body energy status in Holstein-Friesian cows.

Cow energy balance is known to be associated with cow health and fertility; therefore, routine access to data on energy balance can be useful in both management and breeding decisions to improve cow performance. The objective of this study was to determine if individual cow milk mid-infrared spectra (MIR) could be useful to predict cow energy balance across contrasting production systems. Direct energy balance was calculated as the differential between energy intake and energy output in milk and maintenance (maintenance was predicted using body weight). Body energy content was calculated from (change in) body weight and body condition score. Following editing, 2,992 morning, 2,742 midday, and 2,989 evening milk MIR records from 564 lactations on 337 Scottish cows, managed in a confinement system on 1 of 2 diets, were available. An additional 844 morning and 820 evening milk spectral records from 338 lactations on 244 Irish cows offered a predominantly grazed grass diet were also available. Equations were developed to predict body energy status using the milk spectral data and milk yield as predictor variables. Several different approaches were used to test the robustness of the equations calibrated in one data set and validated in another. The analyses clearly showed that the variation in the validation data set must be represented in the calibration data set. The accuracy (i.e., square root of the coefficient of multiple determinations) of predicting, from MIR, direct energy balance, body energy content, and energy intake was 0.47 to 0.69, 0.51 to 0.56, and 0.76 to 0.80, respectively. This highlights the ability of milk MIR to predict body energy balance, energy content, and energy intake with reasonable accuracy. Very high accuracy, however, was not expected, given the likely random errors in the calculation of these energy status traits using field data.

Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets.

With the aim of increasing the accuracy of genomic estimated breeding values for dry matter intake (DMI) in dairy cattle, data from Australia (AU), the United Kingdom (UK), and the Netherlands (NL) were combined using both single-trait and multi-trait models. In total, DMI records were available on 1,801 animals, including 843 AU growing heifers with records on DMI measured over 60 to 70 d at approximately 200 d of age, and 359 UK and 599 NL lactating heifers with records on DMI during the first 100 d in milk. The genotypes used in this study were obtained from the Illumina Bovine 50K chip (Illumina Inc., San Diego, CA). The AU, UK, and NL genomic data were matched using the single nucleotide polymorphism (SNP) name. Quality controls were applied by carefully comparing the genotypes of 40 bulls that were available in each data set. This resulted in 30,949 SNP being used in the analyses. Genomic predictions were estimated with genomic REML, using ASReml software. The accuracy of genomic prediction was evaluated in 11 validation sets; that is, at least 3 validation sets per country were defined. The reference set (in which animals had both DMI phenotypes and genotypes) was either AU or Europe (UK and NL) or a multi-country reference set consisting of all data except the validation set. When DMI for each country was treated as the same trait, use of a multi-country reference set increased the accuracy of genomic prediction for DMI in UK, but not in AU and NL. Extending the model to a bivariate (AU-EU) or trivariate (AU-UK-NL) model increased the accuracy of genomic prediction for DMI in all countries. The highest accuracies were estimated for all countries when data were analyzed with a trivariate model, with increases of up to 5.5% compared with univariate models within countries.

Phenotypic effects of calving ease on the subsequent fertility and milk production of dam and calf in UK Holstein-Friesian heifers.

The effect of calving ease on the fertility and production performance of both dam and calf was studied in approximately 50,000 and 10,000 UK Holstein-Friesian heifers and heifer calves, respectively. The first objective of this study was to estimate the effect of a difficult calving on the subsequent first-lactation milk production by estimating lactation curves using cubic splines. This methodology allows the estimation of daily milk, protein, and fat yields following calvings of differing degrees of difficulty. Losses in milk yield after a difficult calving have been quantified previously; however, estimates are generally restricted to the accumulated yields at specific days in lactation. By fitting cubic splines, gaps (in which the shape of the lactation curve can be merely guessed) between estimations were avoided. The second objective of this study was to estimate the effect of a difficult birth on the subsequent performance of the calf as an adult animal. Even though the calving process is known to involve cooperation between dam and calf, the effect of a difficult calving has, until now, only been estimated for the subsequent performance of the dam. Addressing the effects of a difficult birth on the adult calf strengthens the importance of calving ease as a selection trait because it suggests that the benefit of genetic improvement may currently be underestimated. The effect of calving ease on the subsequent reproductive performance of dam and calf was analyzed using linear regression and with calving ease score fitted as a fixed effect. Dams with veterinary-assisted calvings required 0.7 more services to conception and 8 more days to first service and experienced a 28-d longer calving interval in first lactation compared with dams that were not assisted at calving. Effects of calving ease on the reproductive performance of the adult calf in first lactation were not detected. Losses in milk yield of the dam were significant between d 9 to 90 in milk subsequent to a veterinary-assisted calving, creating a loss of approximately 2 kg of milk per day, compared with a nonassisted calving. Calves being born with difficulties showed a significant reduction in milk yield in first lactation, demonstrating the lifelong effect of a difficult birth. Compared with nonassisted calves, veterinary-assisted calves showed a loss of 710 kg in accumulated 305-d milk yield, which was significant from 129 to 261 d in milk. This suggests that from birth to production, physiological effects of a bad calving are not negated. Results furthermore suggest a beneficial effect of farmer assistance at calving on the milk yield of both dam and calf, when moderate difficulties occurred.

The use of mid-infrared spectrometry to predict body energy status of Holstein cows.

Energy balance, especially in early lactation, is known to be associated with subsequent health and fertility in dairy cows. However, its inclusion in routine management decisions or breeding programs is hindered by the lack of quick, easy, and inexpensive measures of energy balance. The objective of this study was to evaluate the potential of mid-infrared (MIR) analysis of milk, routinely available from all milk samples taken as part of large-scale milk recording and milk payment operations, to predict body energy status and related traits in lactating dairy cows. The body energy status traits investigated included energy balance and body energy content. The related traits of body condition score and energy intake were also considered. Measurements on these traits along with milk MIR spectral data were available on 17 different test days from 268 cows (418 lactations) and were used to develop the prediction equations using partial least squares regression. Predictions were externally validated on different independent subsets of the data and the results averaged. The average accuracy of predicting body energy status from MIR spectral data was as high as 75% when energy balance was measured across lactation. These predictions of body energy status were considerably more accurate than predictions obtained from the sometimes proposed fat-to-protein ratio in milk. It is not known whether the prediction generated from MIR data are a better reflection of the true (unknown) energy status than the actual energy status measures used in this study. However, results indicate that the approach described may be a viable method of predicting individual cow energy status for a large scale of application.

Genetic parameters for carcase measurements and age at slaughter in commercial cattle.

Genetic parameters were estimated for cold carcase weight (CCW), carcase conformation (CON), carcase fat class (FAT), age at slaughter (AGE) and average daily carcase gain (ADCG) in 14 common UK breeds of cattle. These included crossbred animals but purebred datasets were also analysed for the most populous sire-breeds. Heritability estimates for beef breeds that were significant ranged from 0.24 to 0.44, 0.12 to 0.35, 0.12 to 0.36, 0.15 to 0.38 and 0.26 to 0.43 for CCW, CON, FAT, AGE and ADCG, respectively. For Holstein-Friesian, a dairy breed, heritability estimates were consistently lower than most beef breeds with estimates of 0.12, 0.13, 0.13, 0.06 and 0.15 for CCW, CON, FAT, AGE and ADCG, respectively. In all breed groups, genetic correlations were positive between CCW, CON and ADCG. In general, genetic correlations were moderate between CCW and CON (0.13 to 0.77), moderate to strong between CCW and ADCG (0.57 to 0.98) and weak or moderate between CON and ADCG (0.12 to 0.82). Genetic correlations for FAT with CCW (- 0.20 to - 0.42) and CON (- 0.16 to - 0.52) tended to be negative in the beef breed but were positive in the dairy breed, although not significant between CCW and FAT. For most beef breeds genetic correlations between AGE and carcase traits were not significant with the exceptions of AGE and CCW for Simmental (- 0.15) and Salers (- 0.24), AGE and CON for Limousin (0.15) and Simmental (0.14) and AGE and FAT from three sire-breeds (- 0.17 to - 0.35). However, the correlation between AGE and ADCG was negative and moderate to strong in magnitude (- 0.23 to - 0.67) in all beef breeds as expected since faster-growing animals reach slaughter age earlier. For Holstein-Friesian, all genetic correlations with AGE were negative and moderate to strong. Genetic correlations indicate that selection for increased carcase weight should simultaneously increase growth rate and improve conformation in all breeds and reduce carcase fatness in the majority of beef breeds. The results indicate that there is genetic variation in all five traits suitable for undertaking genetic improvement of carcase traits and age at slaughter; however, there are apparent breed differences. The use of abattoir-derived phenotypes for undertaking genetic improvement is an example where the supply chain can work together to share information to enable the cattle industry to move forward.

Specific antibodies to soluble alpha-synuclein conformations in intravenous immunoglobulin preparations.

Alpha-synuclein is the major protein in Lewy bodies, the hallmark pathological finding in Parkinson's disease (PD) and dementia with Lewy bodies (DLB). Although normally intracellular, it also can be secreted, so extracellular alpha-synuclein may contribute to neuronal injury. Serum antibodies to alpha-synuclein could exert protective effects by increasing alpha-synuclein's movement out of the brain and, if they cross the blood-brain barrier, by inhibiting its neurotoxic effects. The objective of this study was to measure antibody concentrations to alpha-synuclein monomer and soluble oligomers in three intravenous immunoglobulin (IVIG) preparations, Gamunex (Talecris Biotherapeutics), Gammagard (Baxter Healthcare) and Flebogamma (Grifols Biologicals). Antibodies were measured in native IVIG preparations and after antibody-antigen complex dissociation. IVIG's non-specific binding was subtracted from its total binding to alpha-synuclein to calculate specific anti-alpha-synuclein antibody concentrations. Specific antibodies to alpha-synuclein monomer and/or soluble oligomers were detected in all IVIG products. In native IVIG preparations, the highest anti-monomer concentrations were in Gammagard and the highest anti-oligomer concentrations were in Gamunex; the extent to which lot-to-lot variation may have contributed to these differences was not determined. Antibody-antigen complex dissociation had variable effects on these antibody levels. The IVIG preparations did not inhibit alpha-synuclein oligomer formation, although they changed the distribution and intensity of some oligomer bands on Western blots. The presence of antibodies to soluble alpha-synuclein conformations in IVIG preparations suggests that their effects should be studied in animal models of synucleinopathies, as a first step to determine their feasibility as a possible treatment for PD and other synucleinopathies.

Short communication: Characterization of the genome-wide linkage disequilibrium in 2 divergent selection lines of dairy cows.

The objective of this study was to describe results of a genome-wide map of single nucleotide polymorphisms (SNP) and assess the linkage disequilibrium (LD) level in 2 divergent selection lines of dairy cows. DNA extracted from 299 Holstein cows was used to determine genotypes in 54,001 SNP loci using the BovineSNP50 array (Illumina Inc., San Diego, CA). Animals were from 2 genetic lines (166 genetically selected for fat and protein yield vs. 133 controls) raised on an experimental farm. Data edits removed loci with a major allele frequency greater than 0.95, genotypes in fewer than 100 cows, and missing valid chromosomal assignment or position. After edits, 41,859 loci (77.5% of the original total) were kept for further analysis. Linkage disequilibrium (LD) values were calculated for all possible syntenic SNP locus pairs located within intervals of 1 million base pairs, as the squared correlation between alleles. Pairwise haplotypes were determined using parsimony. Linkage disequilibrium was calculated for all animals and then for each genetic line separately. The average LD calculated across all chromosomes was 0.069, 0.071, and 0.075 for all, control, and select line cows, respectively. Genetic line had a statistically significant effect on LD. Of all locus pairs studied, 53,487 to 95,279 (depending on the data set) were in LD >0.30, which may be considered the minimum useful for mapping purposes and genomic selection. Useful LD was mostly found between adjacent pairs located within 30,000 to 50,000 bases. A few locus pairs (844-1,070 in the 3 data sets) were found in almost perfect (>0.99) LD. The overall product-moment correlation of LD values between the control and select lines was 0.79 (significantly different from 1), ranging from 0.71 to 0.84 for different chromosomes. Looking at this correlation by SNP pair distance revealed that persistence of LD phase across the 2 lines extended chiefly for 200,000 bases. Selection is likely to have affected LD in the studied cow population. These results may be useful to gene detection and genome-wide association studies.